MURU MUSIC
Founder and Chief Musicologist Nicc Johnson
At age 16, Nicc Johnson began his career as a DJ with the dream of eventually working in the international electronic music hub of Ibiza, Spain. With an unprecedented level of drive and determination, he would exceed that goal shortly to become the resident DJ at Ibiza’s most famous club, Pacha, for seven years, and move on to consult for restaurants, curating and creating music for playlists all around the world.
What eventually slowed Johnson down was his frustration over the tools available to people who are interested in making flowing playlists, whether for work or simply for their own listening pleasure. And so, Muru Music was born. Johnson set out to create the world’s best AI-driven music-recommendation-and-discovery engine to help streaming platforms keep the music flowing better than ever. By combining one of the tech industry’s most savvy deep-learning algorithms with one of the world’s foremost DJs, they’re just getting started.
Johnson took a moment out of his highly sought-after time to chat with us about the company and its mission to change the way we discover and organize music.
Click here for the full Entrepreneurship in Music series.
You’ve been all over the music-career spectrum (musicologist, DJ, sound engineer, consultant, curator…). Is there anything you haven’t you done?!
Ha, I guess you could say that, yet somehow, I feel like I’m only just getting started.
I was born in Amsterdam but raised in Ibiza. I studied music theory and guitar from a young age but suffered from terrible stage fright at my first-ever performance and decided to quit shortly after. I was that kid at school that always had his earphones on listening to music rather than talking to other students. In my early teens, I became obsessed with DJing, and by the time I was 13, I had saved up enough money doing odd jobs for our neighbors to be able to buy my first pair of belt drive “Acoustic Control” turntables. DJing became an obsession and it was my re-entry into music, and for some strange reason, I no longer had stage fright.
By the time I was 16, I was making a living as a professional DJ. Over my 16-year career, I touched on many other areas of the music industry, including A&R, bookings, event management, promotion, music curation, and ultimately a seven-year club residency with Pacha Ibiza.
Although I loved being a DJ, I wanted to go beyond the playing and really understand how music worked. When I turned 18, I had the opportunity to move to Madrid and study Sound Engineering and Production. Although very insightful, it still wasn’t enough — I was hungry for knowledge and wanted to understand how a DJ could connect with an audience, so I delved into the world of Musicology to understand how music affects and influences our brain.
It feels as if everything I’ve worked on in the last 18 years has been leading up to this moment. The pieces of the puzzle have come together, and I’m using everything I’ve learned and putting it into Muru. I want to contribute to the future of music technology and how we use Artificial Intelligence.
“We are dealing with music here, not 1s and 0s, so we made the early decision to not trust the data. We engineered the user experience and worked our way back to the data. When we were faced with a problem, we looked at music theory and Djing to solve the user experience rather than rely on what the data was telling us.”
So that was the inspiration for the company or its genesis?
Muru was initially created to solve a professional problem I had as a music curator. As a consultant, I was creating these incredibly long playlists for small venues like bars, restaurants, hair salons, etc. The very initial idea for Muru was to build a search tool that would help me find more songs with the same “vibe” as the ones I had already found. If a Jackson 5 song worked really well in a previous playlist, I wanted to find songs with a similar vibe for the next playlist.
At the time (six years ago), that literally meant listening to hours of music until I found the right song. The aim of the tool was quite simply to speed up the creation process and improve the quality of my playlists.
As soon as I started to scope it out, I very quickly realized that it would be even better if I could build a tool that would allow my clients to create their own playlists — let them be in charge of their music journey.
My playlists generally had a progression in them. It wasn’t just a random set of songs thrown together, but rather, a journey with songs that connected somehow. I knew that aspect was key for our tool, which ultimately meant we needed to codify the DJ part of my brain. A.I. was still in its early days, particularly for music, but we were confident we could pull it off. However, as we went down the rabbit hole, we came across a very big problem…
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What was that problem?
Imagine for a moment the Library of Congress. It’s the largest collection of books and documents in the world, all perfectly categorized. Imagine, one morning, a group of people go into the library and spend all day moving thousands of books and documents around, swapping or removing the labels. Chaos!
Music data has a similar problem. As such, it’s impossible for any digital catalog owner, to know exactly what music they have in their database. Without knowing what music you have or what new music you’re adding — your recommendation, discovery, and curation will always suffer.
At a time where we have access to all this music, the experience should be amazing. Genres play a big part in that. Identifying what genre each individual song belongs to helps us identify songs with a similar “vibe.” Today, classification of songs is still a manual process. With 20,000 new songs being added to streaming services every day, it’s a very big problem that’s only getting bigger.
We’ve used A.I. with the fundamentals of DJing to create the first automagical algorithm that can classify any song into the correct genre with 99% accuracy in a matter of seconds.
The Muru mission is to become the industry standard for music classification so that 100% of any music catalog is properly classified and so recommendation and discovery engines can be optimized and personalized for each individual user. We want to revolutionize music streaming to increase revenues for artists and platforms and improve the user experience for consumers.
“We want to help people become better listeners and help them identify and understand the music they actually love, so they can confidently find more of it.”
So, this is a B2B, or is there potential to become both B2B and B2C?
We are a B2B SaaS business that builds IP, algorithms, and music-playback features that can be integrated into a range of products like speakers, smartphones, computers, and headphones but also consumer brands like Facebook and Snapchat. Our classification engine benefits streaming services and publishers. I’m especially excited about what our tech can do in combination with NLP (Natural Language Processing, voice control) for the visually impaired and even the automotive industry.
Like any piece of machine-learning tech, you’re bound to run into some bugs and kinks. What problems have appeared in beta versions of the tech that have needed to be worked out in further iterations?
Absolutely. We didn’t have a blueprint on how to build any of our tech, so there was a lot of trial and error. We were an incredibly lean team for the first three years as we experimented, tested, and iterated our tech to a point that we could prove that our algorithms worked! There were plenty of sleepless nights and countless hours of staring at spreadsheets before we got our classification right. We aren’t done yet either, but most importantly, we have proven that the technology works and, now, we are taking the A.I. to the next level.
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How did you work out those kinks?
Funnily enough, the biggest breakthrough came with our approach. We are dealing with music here, not 1s and 0s, so we made the early decision to not trust the data. We engineered the user experience and worked our way back to the data. When we were faced with a problem, we looked at music theory and DJing to solve the user experience rather than rely on what the data was telling us.
We built all the various elements of our Algorithms separately and then slowly pieced them together allowing for better quality control.
You have worked as a DJ in one of the world’s best clubs. How did you, yourself, learn to put songs together in meaningful, impactful ways?
I get asked this question a lot and, to most people’s frustration, I have to say it comes down to experience. A good DJ specializes in two things: song selection and timing. That skill is only learned by DJing an insane amount of hours in front of hundreds of different audiences, observing and understanding what works and, more importantly, what doesn’t.
Growing up in Ibiza, I was fortunate enough to learn from the best. I would sneak into clubs at a very early age and spend the whole day (and sometimes night) next to the DJ booth, absorbing everything. I’d remember every track the DJ would play, the order they would play it in, when they would mix it, and then observe the crowd’s reaction. I did this for years and then started experimenting myself. Any DJ that tells you they always play an amazing set… is lying. As a DJ, you make plenty of terrible music decisions, but it’s exactly that which gives you the skills to be better, more confident, and, ultimately, a great selector.
In a way, do you see Muru as something that’s going to help people become better “DJs”?
It absolutely could make “lazy” DJs better selectors, however, that is not our focus. We want to help people become better listeners and help them identify and understand the music they actually love, so they can confidently find more of it.
My personal mission is to make the music experience amazing for every human being in the world with a smartphone — starting with the visually impaired!
You say that your API could save Spotify and Pandora millions per year. Why is that?
As I mentioned before, streaming services ingest about 20,000 new songs every single day, and the classification of those songs is still a manual process. We can accurately classify all songs before they are ingested into a music catalog so that they can be part of the recommendation and discovery algorithms immediately. More importantly, we can identify duplicate songs, erroneous artist profiles, and many other things.
Most platforms have large engineering teams dedicated to all of this. We have a pretty good idea of how much money it’s costing these platforms every year, and we’re confident we can reduce that cost by at least 80%. At a time where profitability is key, I’d say we have a pretty appealing offer.
How did your networks around the world, and the music scene, help to kickstart this company?
I was met with a lot of pushback and resistance when I first started Muru. People didn’t really identify with the problem as it was somewhat technical and they thought I was trying to compete with streaming services by building a playlist tool. The word “crazy” was thrown around a few times, but once I got validation from a few investors and industry experts, the conversation changed, and those in my network were happy to start making introductions to the right people. I’m forever grateful for the first investors that took a leap of faith in Muru before I was able to prove that the tech worked.
“Any DJ that tells you they always play an amazing set… is lying.”
How would you describe your corporate-team culture?
We’ve been bootstrapping since day one, so it’s too soon to tell — but we are growing our team by 200% in the next year, so company culture is already on my mind. Everyone that joins our team is a music lover, but more importantly, we look for people that are passionate about what we’re doing and want to achieve amazing things. I’m a firm believer in transparency and honesty as key ingredients to building a trusting and productive environment where team members can really feel comfortable to take risks and come up with new and innovative ways to address the problem we’re solving.
What advice would you give young artists or entrepreneurs?
It sounds like a massive cliché, but the best advice I’ve been given and adhere to is believing in myself, being genuine and nice. If you believe in who you are, what you want to achieve, and visualize it, you can do amazing things.
When I was 17, I wrote down my DJ goals on a piece of paper. I wanted to play at “Space” or “Pacha” Ibiza. I never got to play Space, but I did become a resident DJ at Pacha for seven years. My advice: If you believe in yourself and are passionate and genuine, you will achieve everything you want.
If you’re interested in hearing more about pioneering new music initiatives, check out the full Entrepreneurship in Music series!
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